PROJECT SUMMARY Cortical circuits generate dynamic patterns of activity. One of the great challenges of modern neuroscience is to determine the circuit architectures that generate such dynamics patterns, and understand their genesis and functional significance. Most research on brain dynamics focused on stable patterns of activity showing continuous transitions (e.g., oscillations). However, in recent years there has been an increased interest on transient dynamics, including the ones resulting from the sequential switching between metastable states. Extracellular recordings of cortical ensembles indicated that sequences of metastable states, characterized by correlated changes in activity can be detected across subpopulations of neurons. Metastable states have been associated with specific cognitive or sensory variables, suggesting an important role for brain function. Metastability was also observed in the absence of any behavior or stimulation ? suggesting that metastable states may be generated locally and may reflect intrinsic architectures of cortical circuits. Despite evidence for their functional significance, little is known about metastable dynamics in cortical circuits. Indeed, lack of a coordinated and systematic approach to study both temporal and spatial signatures of these patterns has limited progress in this area. This proposal aims at developing an integrated experimental-computational platform for detecting metastable dynamics in cortical ensembles, inferring the circuit organizational principles underlying them, and understanding how plasticity affects metastability. Our team is formed by six PIs with complementary expertise in the experimental and computational approaches necessary to successfully accomplish this program. We will focus on circuits in the superficial layers of the gustatory portion of the insular cortex, a well-established model for understanding metastability. Our long-term goal is to generalize our findings to the study of transient dynamics in other cortical areas and understand their relevance for sensory, motor and/or cognitive tasks. Successfully accomplishing the proposed research will allow us to identify universal principles of collective network dynamics underlying behavior and experience-dependent learning.